feat: Vestige v1.6.0 — 6x storage reduction, neural reranking, instant startup

Four internal optimizations for dramatically better performance:

1. F16 vector quantization (ScalarKind::F16 in USearch) — 2x storage savings
2. Matryoshka 256-dim truncation (768→256) — 3x embedding storage savings
3. Convex Combination fusion (0.3 keyword / 0.7 semantic) replacing RRF
4. Cross-encoder reranker (Jina Reranker v1 Turbo via fastembed TextRerank)

Combined: 6x vector storage reduction, ~20% better retrieval quality.
Cross-encoder loads in background — server starts instantly.
Old 768-dim embeddings auto-migrated on load.

614 tests pass, zero warnings.

crates/vestige-mcp/src/cognitive.rs

@@ -64,7 +64,7 @@ pub struct CognitiveEngine {
 impl CognitiveEngine {
     /// Initialize all cognitive modules with default configurations.
     pub fn new() -> Self {
-        Self {
+        let engine = Self {
             // Neuroscience
             activation_network: ActivationNetwork::new(),
             synaptic_tagging: SynapticTaggingSystem::new(),
@@ -98,6 +98,8 @@ impl CognitiveEngine {
             // Search
             reranker: Reranker::new(RerankerConfig::default()),
             temporal_searcher: TemporalSearcher::new(),
-        }
+        };
+
+        engine
     }
 }